Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 10; pp. 7086 - 7096
• Main Authors: Yun, Won Joon, Park, Soohyun, Kim, Joongheon, Shin, MyungJae, Jung, Soyi, Mohaisen, David A., Kim, Jae-Hyun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Closed circuit television; Deep learning; Electronic mail; Image resolution; Machine learning; Multi-agent systems; Multiagent systems; neural networks; Optimization; Reliability; Surveillance; Surveillance systems; Uncertainty; unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3143175

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments.This article creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimizing overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This article develops a multiagent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this article employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.